Kedro

The Friendly Guide to Powerful Data Science Pipelines

Introduction: Why You Need Kedro in Your ML Toolkit

Ever built a machine learning model that works great in your notebook, but falls apart in production? We've all been there! The path from prototype to production can be messy—full of untracked datasets, ever-changing hyperparameters, and tangled script folders. That’s where Kedro steps in.

Kedro is an open-source Python framework designed to bring order to the chaos of experimental data science and machine learning workflows. By applying software engineering principles—like modularity, maintainability, and reproducibility—Kedro makes building, iterating, and deploying ML pipelines a breeze.

What Is Kedro?

Kedro lets you design, build, and run modular data pipelines using:

• Nodes: The smallest unit of work (a function that does something to your data) 
• Pipelines: Ordered collections of nodes connected together 
• Data Catalog: Centralized config for all your datasets (inputs, outputs, intermediates)
• Project Template: A standardized project skeleton so your code stays clean and scalable You spend less time on "plumbing" and more time solving real business problems.

Kedro Architecture—How It All Fits Together

Architecture diagram showing Kedro project: nodes, pipelines, data catalog, configuration folders, data folders, etc.

A typical Kedro project looks like this:

• conf/: Configuration files (data sources, parameters, credentials) 
• data/: Raw, intermediate, and output data; organized by processing stage 
• src/: Your source code—where you define nodes and pipelines 
• logs/: Pipeline run logs 
• notebooks/: Jupyter notebooks for exploration 
• docs/: Documentation (e.g., README, pipeline diagrams)

This means every aspect of your workflow is versioned, reproducible, and easy to audit.

Defining Pipelines: Nodes, Pipelines, Runners, and More

Let’s walk through a typical Kedro workflow:

1. Configure Data Sources and Parameters

You define all inputs, outputs, and parameters in YAML config files, so everything’s centralized.

conf/base/catalog.yml example:

Parameters in conf/base/parameters.yml:

2. Write Your Node Functions

Each node is a pure Python function—just input data and output transformed data.

Example node:

3. Wrap Functions into Nodes

Organize your functions into nodes that Kedro can sequence.

4. Link Nodes into a Pipeline

Combine your nodes in the order they should run.

Block diagram showing a pipeline: nodes connected together by data outputs/inputs

5. Run the Pipeline!

With your pipeline defined, launching it is as simple as: 

    kedro run

Kedro reads your configs, loads your data, runs each node in order, and saves outputs— automatically tracking the full workflow.

Example Project: Sentiment Classifier for Movie Reviews

Let’s say we’re tackling sentiment analysis with IMDB reviews. Here’s how Kedro helps:

• Data Preparation: Extract, clean, and merge labeled data. 
• Feature Engineering: Text vectorization, tokenization, etc. 
• Model Training: Wrap training as a node, track parameters and artifacts. 
• Model Evaluation: Evaluate using held-out test set—outputs metrics and plots.

Each step is a node. Nodes are connected into pipelines (e.g., data engineering → data science → evaluation).

Example block diagram of IMDB sentiment classifier pipeline: raw data → cleaning → feature engineering → model training → evaluation

Kedro Integrations: MLflow, Airflow, Cloud, and More

Kedro’s plugin system enables seamless integration with:

• MLflow: Experiment tracking, model registry, deployment 
• Airflow: Pipeline orchestration in production 
• Cloud Storage: S3, Azure, GCP connectors for datasets 
• Kedro-Viz: Interactive pipeline visualization for debugging or sharing

You can even switch between dev and prod environments with zero code change, thanks to environment-specific YAML configs.

Kedro vs. Other ML Workflow Tools

Kedro shines in modularity and maintainability—ideal for data teams that value reproducibility and clean code for production deployment.

Best Practices for Intermediate Users

• Start with Small Pipelines: Build one pipeline, then expand modularly.
•  Version Everything: Use Kedro’s catalog and parameters to track data/model changes. 
• Integrate with MLflow: Add experiment tracking for easy model comparison.
•  Visualize Pipelines: Use Kedro-Viz to understand flow and spot bottlenecks. 
• Decouple Functions: Make node functions as pure as possible—no hidden side effects!

Common Challenges & How Kedro Helps

• Scaling Up: Kedro’s pipeline abstractions make it easier to scale projects from prototype to production, though large data volumes may require specialized connectors. 
• Team Collaboration: Standard project structure and code templates mean everyone can understand and extend the codebase.
• Dev to Prod Transition: Environment separation lets you test locally, then deploy seamlessly.

Block diagram showing dev vs prod deployment, plugin integrations

Conclusion and Next Steps

Kedro isn’t just another workflow tool—it’s the organizational backbone your ML projects need. If you're tired of messy notebooks and want best practices baked in, start with Kedro. You’ll be building robust, reproducible, and scalable data science pipelines in no time! 

Ready to level up your MLOps workflow? Try integrating Kedro with MLflow or visualize your first pipeline with Kedro-Viz. Or—enroll in an online course and turbocharge your data science skills.